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Record W4408307974 · doi:10.1111/1911-3846.13026

Governmentality, counter‐conduct, and modes of governing: Accounting and the pursuit of municipal sustainable waste management

2025· article· en· W4408307974 on OpenAlexvenueno aff
Thomas Ahrens, Laurence Ferry

Bibliographic record

VenueContemporary Accounting Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsGovernmentalityAccountingBusinessPublic administrationEnvironmental economicsPolitical scienceEconomicsLawPolitics

Abstract

fetched live from OpenAlex

Abstract Recent research into the uses of accounting as a technology of government has used Foucault's notion of “counter‐conduct” to shed light on various ways in which the governed can seek to alter the regimes to which they are subjected. This paper unpacks the notion of counter‐conduct further in order to develop a clearer conceptualization of how regimes of government can change over time, with or without clearly identifiable attempts by the governed to influence such changes. We develop our argument based on a longitudinal field study of sustainable waste management practices in a municipality in the English East Midlands. We track the municipality's attempts to become more sustainable in the context of an evolving central government performance management regime that went through a series of legislative and administrative iterations—namely, Best Value, Comprehensive Performance Assessment, and Comprehensive Area Assessment. We conceptualize these iterations of central performance management and the related changes in local government practices and technologies of governing as a series of overlapping “modes of governing” (Bulkeley et al., 2007, Environment and Planning A , 39 (11), 2733–2753). We suggest that accounting research can benefit from the notion of modes of governing because it sheds light on the theoretically expected, but empirically underresearched, copresence of multiple rationales, programs, and technologies of governing, all operating at the same time.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.065
GPT teacher head0.373
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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